GE uses big data to power machine services business

GE, one of the UK's largest manufacturers, is using big data analytics to predict maintenance
needs.

GE manufactures jet engines, turbines and medical scanners. It is using operational data from
sensors on its machinery and engines for pattern analysis.

GE is using the analysis to provide services tied to its products, designed to minimise downtime
caused by parts failures. Real-time analytics also enables machines to adapt continually and
improve efficiency.

Bill Ruh (pictured) is vice-president for software at GE Research. Recruited from Cisco two
years ago, Ruh has a customer-facing role, looking at the business opportunities enabled through
the industrial internet.

Ruh said: "The airline industry spends $200bn on fuel per year so a 2% saving is $4bn. GE
provides software that enables airline pilots to manage fuel efficiency."

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Another product, Movement Planner, is a cruise control system for train drivers. The technology
assesses the terrain and the location of the train to calculate the optimal speed to run the
locomotive for fuel economy.

Software developed at GE is now being used by a Canadian electricity supplier to prune back
trees cost-effectively along its electricity distribution lines. Vegetation falling on power lines
is the biggest cause of electricity outages, according to Ruh.

One third of GE's business is about servicing equipment. He said: "Most customers do planning in
the rear-view mirror, which is almost always wrong." He said heavy industry can make considerable
savings moving to zero downtime and this is the main focus of his work.

"We invested $1.5bn over four years to develop services and create new software. We are working
on making devices more intelligent using sensors; and controllers that can be configured in real
time," he said.

For instance, in a wind farm the wind turbines at the front affect the turbines behind
them. This may lead to vibrations causing a failure due to a stress fracture on the turbine's
blades. "We can adjust turbines based on the wind. We adjust the blade in real time to avoid
vibration." As a result he claims GE is able to deliver a 2 to 5% improvement on the efficiency of
the wind farm.

He said the amount of data generated by sensor networks on heavy equipment is astounding. A
day's worth of real-time feeds on Twitter amounts to 80GB. "One sensor on a blade of a gas turbine
engine generates 520GB per day, and you have 20 of them."

Predictive analytics is something IT systems management tools aspire to through self-healing and
autonomous computing. Ruh said: "The industrial world never picked up on datacentre automation."
However, now there is a greater demand for such technology as people with many years of experience
retire.

Behind the scenes

In Ruh's experience, the internet of sensors is very different to the internet used by humans.
He said: "The internet is optimised for transactions, but in machine-to-machine communications
there is a greater need for real time and much larger datasets." Cloud computing in its present
form is not wholly suitable for these kinds of machine-to-machine (M2M) interactions. "We are
seeing more processing on machines, due to latency," he explains.

The technology is based around in-memory database systems. Since the datasets are extremely
large he said GE uses NoSQL and Hadoop. "We have also developed our own database for time series
analysis." He said GE is also working with Microsoft Azure and Amazon Web Services to investigate
how to offload processing to the cloud.

The machine, like a human, need to know about its location and is capable of posting status
updates. Some may even be "friends" in the social networking sense. According to Ruh, a GE engine
on an aircraft could alert ground engineers when the lane has landed, through its social network of
engineer friends. Potentially, this would allow the engineers to be alerted that the aircraft is on
the ground, so the ground team can carry out any maintenance that the engine has alerted them about
through status updates.

Given the potential for a StuxNet attack, it is no
surprise Ruh is concerned about the security impact associated with M2M communications and
intelligence in machines. His biggest concern is the insider threat. He said the machine needs to
understand about the user, the human who is accessing its controls. This suggests it requires
authentication and sophisticated role-based security, the kind of technology associated with
commercial operating systems. Ruh points to Apple as an example of how security can be managed. He
say: "Apple defined and locked down iOS ahead of time. We can apply such a sandbox approach in our
system."

On the user interface side, he said GE has standardised on HTML5, but at lower
layers of the software stack, the company works with Android, Windows and Linux. Ruh predicts that
machines will have apps, rather like iPhone and Android apps to extend their functionality,
although the programming model will be very different, and clearly there will be a need for a
robust security framework before an app will be allowed to run on a machine.

The role of data scientists in the future

GE has established a 200-person site in Silicon Valley specialising in developing services,
powered by software and sensor networks to support its vision of zero downtime and real time
controls for machine efficiency. While it is a software centre, and programming is key to the
development of these new services. Ruh is seeking new skills.

He said: "We need data
scientists. It is the most in-demand skillset and we're looking for a certain kind of person."
According to McKinsey, 1.5 million data scientists are needed globally.

"The technology providers gave us a solution to business intelligence but analytics is a lot
harder than people think. Ten to 15 years ago people were told BI gives greater insight, but the BI
tools have been used just for better reporting." This is where the data scientists role fits. Ruh
is looking not merely for great mathematicians or engineers, but for people who can understand the
meaning of the data sets when applied to a machine operating in the real world.

Two years ago IBM demonstrated how its Watson
supercomputer could outsmart a human by winning the US game show, Jeopardy.
No matter how sophisticated such a machine or how it is programmed, Ruh does not think it can
answer the kind of questions Ruh' team of technology researchers are trying to crack. He said:
"Watson cannot tell me when this machine part will break." He said predictive analytics builds upon
Watson-like machine-learning capabilities.

Given GE's footprint in the UK, Ruh is hoping to establish a similar facility in the UK.
Cambridge seems to be the preferred location at the moment.

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